Statistical financial system and method to value patient visits to healthcare provider organizations for follow up prioritization

ABSTRACT

A Statistical financial system to value patient visits to healthcare providers allows healthcare providers to optimize their financial outcomes when billing insurance payers for services rendered in the course of patient visits. Commercial healthcare information systems today lack the ability to value these services in advance. Given the complexity of valuing services based on many variables and resource limitations, a predictive valuation provides the user with the information for accurate and timely decision making. Using the historical data, the system creates a predictive model of future claims by creating attribute patterns from the data. As new information comes into the system, these attribute patterns are compared to the real-time data in order to create a value for a service and an overall visit. Using the valuation, the system will now be able to prioritize all follow-up tasks. The overall result is a better financial performance for the healthcare provider.

FIELD OF THE INVENTION

This invention relates in general to statistical financial system andmethod to value patient visits to healthcare provider organizations forfollow up prioritization, and more particularly, to methods and systemsof providing valuation of healthcare provider services and visits in thefuture or present within the context of a healthcare analytical systemusing historical statistical methods and the utilization of thevaluation for work prioritization.

BACKGROUND OF THE INVENTION

The nature of healthcare businesses requires that healthcareorganizations perform a financial clearance prior to or when the patientarrives at their facilities for their services. The amount of work andtasks associated with each healthcare organization's service can easilysurpass the number of resources available to properly address all thefollow up tasks and issues. When issues or notifications are notaddressed in a timely manner, it could result in the reduction orrejection of payment for the service, thus impacting the financialoutcome for the organization. There are different ways thatorganizations could prioritize the work based on the type of service orprocedure or the amount charged for the service. However, because of thenature of the healthcare business, the true monetary value of a servicecan vary widely depending upon the type of payer, the patient orsubscriber's employer, and the location where the services are beingdelivered. Different payers' contractual rules result in differentvalues for the same services. In most cases, the type and nature of ahealthcare visit makes it very difficult to properly identify and valueall of the services associated with a healthcare visit.

Moreover, currently available healthcare scheduling, registration, andother healthcare information systems do not provide a way to manage andprioritize and resolve activities and based on a monetary value of theservices for a patient visit to a healthcare provider prior to the pointin time that such services are rendered. This system provides apredictive valuation for the provided services prior to the performanceof those services.

Thus, there is a need for Statistical financial system and method tovalue patient visits to healthcare provider organizations for follow upprioritization. Healthcare provider organizations have a criticalfinancial need to identify problems with patient registrations andcorrect those problems in a timely fashion in order to prevent denialsand rejections by the insurance company payers. Given time and resourceconstraints, it is important to not only identify the correct issues,but to prioritize and track those issues. A prioritization must be basedon financial impact and the valuation of the visit based on services isan important feature. Again, given the time and resource constrainsinvolved, having a predictive method to assign a value to servicesbefore they are rendered based on statistical methods, and thus creatinga value for the patient visit is a unique and innovative solution.

SUMMARY OF THE INVENTION

A statistical financial system and method to value patient visits tohealthcare provider organizations for follow up prioritization willallow healthcare providers to optimize their financial outcomes whenbilling insurance payers for services rendered in the course of patientvisits to said providers. Commercial healthcare information systemstoday lack the ability to value these services in advance and put thehealthcare provider at a tactical disadvantage. Given the fullcomplexity of valuing services based on many variables such as theinsurance contract, geographic location, physician, etc., as well as alimitation in both time and resources, a predictive valuation providesthe healthcare provider user with a huge advantage with the informationnecessary for accurate and timely decision making A statistical databaseis created based on historical healthcare claims information. Using thehistorical data, the system creates a predictive model of future claimsby creating attribute patterns from the data. As new information comesinto the system in the way of patient registrations, these attributepatterns are compared to the real-time data in order to create a valuefor a service and an overall visit. By having a valuation and a set ofbusiness exceptions in the form of a user alert, the system will be ableto prioritize how follow-up tasks should be worked and can performvaluable decision support for the user, who can focus on the actualtasks to perform. The overall result is a better financial performancefor the healthcare provider.

In one set of embodiments, a method to utilize a statistical databasethat combines the data from bills submitted by the healthcareorganizations to the different healthcare payers, the healthcare payers'detailed explanation of payments for each service and detailed data forthe patient visit to create template of attribute patterns for futurepredictions.

In another set of embodiments, the historically-generated attributepatterns are matched with attribute patterns from new visits toestablish an estimated monetary value for the new visits. Once a patientvisit to a healthcare provider is assigned a monetary value, the systemassociates such a monetary value to other business rules to create avalue processing weight for the financial clearance for decision making,prioritization of follow up tasks and issues.

In still another set of embodiments, a method for using the valuation ofthe listed services for the visit in order to value the overall visit,expose related issues to the provider organization, and provide aprioritization of issues based on a monetary value.

In other similar embodiments a method to continuously monitor theidentified and prioritized tasks and issues to resolution in order tomaximize financial outcome.

While the specified embodiments are unique, they will exist within asystem defined by prior art in a commercial healthcare software solutionentitled Revenue Orchestrator from DaVincian Technologies, which hasexisted in various releases since 2003. The technology to detect issuesbased on knowledge rules and provide an alert to a user is prior art ofDaVincian Technologies.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theinvention, as defined in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the specified system and method.The present system and method is illustrated by way of example and notlimitation in the accompanying figures. It should be noted that elementsin the figures are illustrated for simplicity and clarity and have notnecessarily been drawn to scale.

FIG. 1 includes an illustration of a hardware and network configurationof a statistical healthcare financial system and method in accordancewith a specific, non-limiting embodiment.

FIG. 2 is a representation of logical layers used in certain statisticalhealthcare financial systems.

FIG. 3 is a data flow/process diagram of an embodiment of a statisticalfinancial system and method to value patient visits to healthcareprovider organizations for follow up prioritization.

FIG. 4 includes a flowchart diagram of a statistical financial systemand method to value patient visits to healthcare provider organizationsfor follow up prioritization in accordance with an embodiment of thesaid system and method.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A few terms are defined here to aid in the understanding of thedescriptions and drawings that follow.

The term “healthcare provider” or simply “provider” represents theentity that provides medical care to a patient in return for some fee,which may be billed to an insurance company, the patient, the patient'sguarantor, or some combination. The provider may be a hospital (alsoknown as a facility), a clinic, or a doctor's office.

The term “healthcare provider visit” or just “visit” means a uniquepatient visit to a healthcare provider. During the actual visit, a setof services are provided to the patient for fees. These fees aredetermined by many different factors including, the free market, theprovider, the location of the provider, the doctor, the patientdiagnoses, the line of business of the care (e.g. inpatient, outpatient,emergency, etc.), the length of hospital stay, whether care wasprovided, and most importantly, the insurance that the patient is usingfor the visit since there are pre-negotiated rates and discounts betweenthe provider and the insurance company. The data capture of a new visitfor analysis purposes may start in advance of the first date of servicefor the visit as many providers use a combination of scheduling andpre-registration processes. Typically, the data elements capturedinclude, but is not limited to the patient demographic information, dateof the appointment, provider information, insurance information if thepatient is insured, diagnostic information, and medical procedureinformation.

The term “alert” is the data representation of an issue in the data thatneeds to be brought to the attention of a user or an automated processin order to correct the issue. Individual alerts are configurable andare detected in the analytical processing of the data by either businessor knowledge rules. Business rules are configured process steps thatoperate on the data while knowledge rules are simple Booleanexpressions. The actual definition of an individual alert is based upondefined business processes within the healthcare provider organization.

As used herein, the terms “comprises”, “comprising”, “includes”,“including”, “has”, “having”, or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a methodcomprising a list of elements contains those elements but is notnecessarily limited to those elements and may contain other elements notlisted. Further, unless expressly stated to the contrary, “or” refers toan inclusive or and not to an exclusive or. For example, a condition Aor B equals true if A is true (or present) and B is false (or notpresent), A is false (or not present) and B is true (or present), andboth A and B are true (or present).

Attention is now directed to statistical financial systems and methodsto value patient visits to healthcare provider organizations for followup prioritization. These systems and methods may be used to first createa data model based on existing historical claims and visit data from ahealthcare provider. Further, these systems and methods can be used tocompare real-time visit data, either from an active patient or ascheduled visit in the future to the previously built model in order tocreate a value that can be assigned to both the visit and any resultingissues that are detected within the data after a series of rulesevaluations. Furthermore, said value can be used a part of aprioritization scheme to have a user work on the most important issuesfor the provider from a financial perspective. This is because the itemsthat have a greater financial impact will tend to be the items withheavier weights and this greater priority.

Before discussing the embodiments of the invention defined by theappended claims, exemplary hardware architecture for using saidembodiments is described. FIG. 1 illustrates an exemplary architecturewhere statistical financial system (“SFS”) on computer 120, can be usedto manage both statistical and structured healthcare data residing indatabase tables contained in database storage and managed by databaseserver 140. SFS 120 can be bi-directionally attached to database server140, which is attached to database storage 14. Database storage 14 maybe direct or networked attached to database server 140. SFS 120 andDatabase Sever 140 may be part of a computer network. Note that FIG. 1is a simplification of a hardware configuration.

Within SFS 120, a plurality of computers (not shown) may beinterconnected to each other via a computer network. The computer onwhich SFS 120 may execute can include a plurality of central processingunits (CPU) 122, memory 124, and Input/Output (I/O) 126. I/O 126 mayinclude internal data buses, network cards, a keyboard, monitor,pointing devices, and storage and peripheral cards among others. Thecomputer can be attached to a plurality of storage devices 12 eitherdirectly or via a network attached drive array, such as a storage areanetwork (SAN).

The application client 160, is utilized by a user to interface with theapplication containing SFS 120. As diagrammed, the application client160 connects to the SFS 120 through an interconnected network 11. Thisinterconnected computer network 11 may be a local network or the publicinternet and may run the TCP/IP protocol or other protocols. In analternate embodiment, the application client 160 may reside on the samelocal network as SFS 120, or even within the same computer. There may bea plurality of application clients 160 connected to the SFS 120 serverover a network. The computer on which application client 160 may executecan include a plurality of central processing units (CPU) 162, memory164, and Input/Output (I/O) 166. I/O 166 may include internal databuses, network cards, a keyboard, monitor, pointing devices, and storageand peripheral cards among others. The computer may be attached to aplurality of storage devices 16 either directly or via a networkattached drive array, such as a storage area network (SAN).

Portions of the systems and methods described herein may be implementedin suitable software code that may reside within memory 124, memory 162,CPU 122, CPU 162, or storage devices 12,14, or 16. In addition, theinstructions in an embodiment of said systems and methods may becontained on a data storage device with a different machine readablestorage medium such as a hard disk drive. Alternatively, theinstructions may be stored as software code elements on a disk array,magnetic tape, diskette, CD ROM, DVD, optical storage device, or otherappropriate machine readable medium or storage device.

In an illustrative embodiment of the systems and methods describedherein, the machine-executable instructions may be lines of interpretedor compiled Java or other language code. In another illustrativeembodiment of said systems and methods, the machine-executableinstructions may be rules or simple expressions. Other architectures maybe used. For examples, the functions of any of the systems and methodsmay be performed by different computers than are shown in FIG. 1.Additionally, a computer program or its software components with suchcode instructions may be embodied in more than one machine readablemedium in more than one computer.

In the hardware configuration above, the various software components mayreside on a single computer or on any combination of separate computers.In alternate embodiments, some or all of the software components mayreside on the same computer.

Turning to FIG. 2, the logical layers of a typical statistical financialsystem for healthcare providers are represented. The lowest layerlabeled 210 encompasses the basic persistence layer of the system. Thedatabase schemas 212 contain the fundamental data model upon which theapplication is based and defines tables, relationships between tables,primary and foreign keys, indexes, and data types. The healthcare datais structured and stored in as structured application data 214. Thestructured application data 214 includes all data necessary for thesystem to operate and for the methods to be performed. This data 214includes relational and non-relational table data, XML and other markedup documents, text, object, expressions, rules, and process state data.The system operates on input data from healthcare providers and othersources, represented as Provider/Clearinghouse source data 216. Thisdata may include ANSI ASC X12 data (837, 835, 270, 271, 276, 277, 278,etc), Health Level 7 (HL7 data), delimited text, XML or other machinereadable formats. Once a sample of historical data is processed andanalyzed for statistical patterns, it is stored as statistical data 217for use in other parts of processing. The system will utilize thestatistical data store 217 to create stored attribute patterns 218 tocompare incoming visit data against in order to calculate value data.

The persistence layer contained within layer 210 and within a preferredembodiment of the invention outlined in the attached claims containsmachine readable and executable instructions in the form of complexbusiness rules and simple expressions. The business rules 222 areexecuted within the analytical and rules engine 230. These businessrules 232 provide a flexible and configurable way to augment the machineinstructions from a compiled language such as Java, which also executein the analytical and rules engine 230. The knowledge rules 234 are morebasic logical expressions that a skilled user can add to make additionaldecisions that take into account variables within their uniqueenvironment.

The analytical and rules engine 230 contains the majority of the machineexecutable instructions that process the data inputs (defined in FIG. 3)and implements the methods to provide a statistical valuation of avisit, issue, or alert. In an embodiment of this system, an applicationservices and user interface layer 240 provides a means for a user tointeract with a remote client 250 to operate an application thatincorporates healthcare visits and alerts and the valuations thereof.The remote client 250, which may be on the same server or a differentcomputer or computers attached over a network is an application such asa web browser that connects to the application services and userinterface layer 240 to provide user-facing graphical interfaces to aperson.

Moving on to FIG. 3, The source data comprised of 303, 304, and 306 cancome from multiple external systems including the healthcare providerbilling system 301, the healthcare provider registration system 306, ora claims clearinghouse company 302. The data defined under 303 and 304are typically ANSI ASC X12 837 and 835 files but may be print spoolUB04/CMS1500 format other proprietary and custom text formats. Thebilling data (837, UB/CMS, or custom) contains informational elementssuch as the dates of service, insurance carrier, the insurance plancode, the facility where services were provided, total and line itemcharges, procedure codes (CPT4/HCPCS), diagnosis related group (DRG),diagnostic information, and physician information, amongst others. Theremittance data (835 or custom) has matching information from the billand includes the claim level and line item level payment, adjustment,reason code, and remark code information. In addition to billing andremittance data, there may be visit data associated with the patientregistration. Typically, this is transmitted (but is not limited to) ina Health Level 7 (HL7) format and contains patient information, theservice location, and insurance information, in addition to otherinformation.

The aforementioned external data is loaded and processed by thehistorical claims and remittance module 310. A block of historical data,for example the data from the previous year, is parsed and split up intorelational tables. The historical data is then fed into the statisticalanalysis module 320, which creates the attribute patterns 330. Thestatistical modeling starts with the identification of the independentvariables used in the analysis. Based on the already mentioned datainputs, an exemplary set of independent variables are comprised of theinsurance carrier, plan code, facility, service code, procedure code,primary diagnosis code, physician, and service location. The independentvariables are used to predict the dependent variables, which are theallowed amount, contractual rate, coinsurance rate, total charges, andtotal payments. In the statistical analysis 320 using a large(historical) data set, the independent variables are selected based ontheir predictive power. Since the outcomes of a full historical set areknown, the greater predictive power is where the difference between thepredicted value and the real value have the best Sharpe ratio, theminimum, standard deviation, and the an average approaching zero.

The independent variables are discrete variables and using bivariateanalysis, the statistical analysis 320 creates statistical markers forthe dependent variable, which are comprised of the n (sample size), q1(value that is at the 25^(th) percentile of the matching attributepatterns), median, q3 (value that is at the 75^(th) percentile of thematching attribute patterns), min (minimum value of the matchingattribute patterns), max (maximum value of the matching attributepatterns), sum (sum of the all of the values of the matching attributepatterns), and sum2 (sum of the squares of the values of the matchingattribute patterns). The variables n, sum, and sum2 allow for manystatistical calculations such as variance, standard deviation, and skew.All possible combinations of independent variables are calculated toobtain the marker for each of the dependent variables. The independentvariables are also weighted based on their contribution to theprediction. The attribute pattern generation 320 saves a datarepresentation of the known set of independent variables (attributepatterns) in order to obtain a fast query when needed later in theprocess. The result set of the fast query is analyzed to find the matchthat has the most significant statistical quality based on the targetvaluation desired. This attribute pattern set for fast query is storedin the statistical database 342.

Once the system has been tuned with historical data, the real timevaluation process can begin. The historical tuning can be repeated overtime. The valuation process starts as a new visit comes in from thehealthcare registration system 306 and is processed by the real timeregistration processor 340, which takes the inbound visit data format,parses it, and hands it off to the real time evaluation of the visit andexception handling 350. This module stores a data representation of thevisit and all of its attributes in the data store 342. Using aconfigurable set of rules and expressions, the visit is analyzed for anyerrors or issues. If errors or issues are detected an alert is raisedand associated with the visit. Alerts are stored 351 in the data store342.

After the visit attributes are determined, and any alerts are raised,the real time pattern matcher 360 creates the valuation of the visit andthe alerts. Each visit has a known set of independent variables. Usingthis set, the fast query of an attribute pattern defined by 330 andstored in 332 is queried. The query forms a result set of aggregationvalues and this result set is ordered by the quality of the sample size,standard deviation, average value and the weighted value of eachindependent variable. Once the ordering is complete, the results areevaluated for the markers whereas the best predictive values are chosenand the target predictive value for each dependent variable. There areseveral valuation scenarios that may occur;

-   -   1. Value of the entire visit—The predictive dependent variable        for a valuation of a visit is the total payments. Among the        independent variables selected, the insurance carrier, plan        code, and procedure code are required. The attribute pattern        with greatest sample size and least standard deviation is        chosen.    -   2. Value of the alert—The value is obtained for the entire        visit. The value is then apportioned between the various open        alerts based on the number of alerts, type of alert, and the        configured weight of the alert.

In addition to the examples above, there are several variations that canoccur; the value of an underpayment and the insured patientresponsibility can be determined based on the dependent variable allowedamount. The value of the uninsured patient payment can be determinedbased on the dependent variable total charges.

Finally, turning to FIG. 4, the basic process flow is depicted. Aprocess 410 monitors for inputs 401, 402, and 403, which contains thehistorical visit, billing, and remittance data. If data exists 415, thenthe process 420 loads the historical data, parses it, and breaks it upinto individual data elements. If there is no data, the process 410continues to monitor for changes.

After process 420 loads the data, process step 425 does the statisticalanalysis described previously during the description of FIG. 3. Aspreviously describes, the best independent variables are selected. Thesevariables are comprised of insurance carrier, plan code, facility,service code, procedure code, primary diagnosis code, physician, andservice location. These independent variables are used to predict thedependent variables allowed amount, contractual rate, coinsurance rate,total charges, and total payments. Based on the desired outcome, thebest variables are aggregated into an attribute pattern that can bematched later when new registrations are received. The attributepatterns are saved to the statistical data store 430 in either a highperformance Berkeley file structure or a relational database table.

The process 435 monitors for real time inputs from 407. These inputs arereceived as patient events occur. These events comprise scheduling,pre-registration, registration, update, check-in, and discharge. Ifthere is data 440, the process step 445 reads the data, breaks it apart,and analyzes it against a set of rules and expressions. The datarepresentation of the visit processing is stored in data store 470 asrelational information. If there is no data, the process step at 435continues to monitor for new data.

The rules and expressions in 445 may detect issues and errors in thedata and the decision point 450 evaluates if there are alerts that needto be raised. If there are alerts, the process step 455 creates thenecessary alerts associated with the visit object and stores thesealerts in the data store 470. Control is then passes to the Value Alertsstep 460. In this step, the attribute pattern with greatest sample sizeand least standard deviation is chosen. As previously described in thedescription of FIG. 3, the value of the alerts are derived from theoverall value obtained for the visit, which is then apportioned betweenthe various open alerts based on the number of alerts, type of alert,and the configured weight of the alert. The results are stored in datastore 470. Finally, the overall valuation of the visit is assigned. Thisis based on the attribute pattern with greatest sample size and leaststandard deviation and the result is stored in the data store 470. Theouter process loop will return control to the input monitor 435 to startthe process anew. In decision 450, if there are no alerts, then just thevalue of the visit is created in step 465 and stored in data store 470.

What is claimed is:
 1. Within a computing architecture that includes acomputer system comprising at least one processor, acomputer-implemented method for operating the computing architecture inorder to improve how healthcare services are managed and provisioned byfacilitating generation and selective invocation of an optimized fastquery that is generated using an attribute pattern derived frompreviously aggregated healthcare service data and that is used toprovide high performance statistical analysis when evaluating newhealthcare service data, the method comprising: instantiating aninterface layer that provides an interface for a user to interact with aremote client, including accessing, via the remote client, one or moreportions of healthcare provider data associated with a healthcareprovider visit, the healthcare provider data including at least onepreviously generated attribute pattern associated with one or more pasthealthcare provider visits, the healthcare provider visit comprising acombination of professional services and healthcare items that are to beprovided to a patient at the healthcare provider visit, eachprofessional service and healthcare item having an associated monetaryvalue; determining an attribute pattern that includes one or morevisit-related variables associated with the professional services andhealthcare items that are to be provided as part of the healthcareprovider visit, wherein: the visit-related variables comprise a selectedsubset of independent and dependent variables used when calculating avalue for the current healthcare provider visit based on one or morehistorical healthcare provider visits, the independent variables areselected based on their predictive power according to the at least onepreviously generated attribute pattern, a higher predictive power ismanifest where a difference between a predicted monetary value and areal monetary value has an optimal Sharpe ratio, a minimum standarddeviation, and an average approaching zero, the attribute pattern isdetermined according to one or more rules executed within an analyticaland rules engine that provides a configurable way to augment machineinstructions from a compiled language, the analytical and rules enginebeing configured to take into account environment-specific variablesthat influence the attribute pattern determination, the machineinstructions are executed in the analytical and rules engine along withthe one or more rules; and a data representation of the attributepattern is stored in memory of the computer system; after the attributepattern is determined, generating a fast query that is optimized tospecifically perform one or more attribute pattern queries on thedetermined attribute pattern, the optimized fast query being structuredso that, when executed, it produces a result set that is used tosubsequently identify a match between one or more previously generatedattribute patterns and the determined attribute pattern, and wherein forthe match to be identified based on the fast query's result set, arelationship between the one or more previously generated attributepatterns and the determined attribute pattern is required to satisfy adesignated statistical quality level; after generating the result set byexecuting the optimized fast query on the data representation of theattribute pattern such that the data representation stored in thecomputer system's memory is accessed during execution of the optimizedfast query, using the result set to identify the match by comparing thehealthcare provider data associated with the healthcare provider visitincluding the at least one previously generated attribute pattern to thedetermined attribute pattern to match the at least one previouslygenerated attribute pattern with the newly determined attribute patternin order to assign a monetary value, a weight, or a priority to thehealthcare provider visit; and based on the comparison of the healthcareprovider data to the determined attribute pattern, and prior to thehealthcare provider visit taking place, calculating a predicted monetaryvalue, a weight or a priority for the healthcare provider visit, thepredicted monetary value, weight or priority indicating the order inwhich follow-up tasks performed after the healthcare provider visit areto be carried out for the healthcare provider visit.
 2. The method ofclaim 1, wherein the monetary value, weight value, and priority areassigned to an alert or other detected issue with visit data.
 3. Themethod of claim 1 where historical data is ANSI ASC X12 format.
 4. Themethod of claim 1 where historical data is Health Level Seven (HL7data), Clinical Context Object Workgroup (“CCOW”), or other text-baseddata from a Healthcare Information, Management, Billing, Scheduling, orRegistration system.
 5. The method of claim 1, wherein the saidattribute patterns are derived from a plurality of tuples withcombinations of attributes matching associated statistical variableresults comprising average, mean, maximum, minimum, mode, median,quartiles, standard deviation, variance, standard error, skew and samplesize for charges, contractual discount rates, net payments, copay, andcoinsurance.
 6. The method of claim 1, wherein a data input includes aplurality of attributes for said healthcare visit and its servicescomprising payer, payer plan, employer group, hospital name, department,hospital service, services name, diagnostics, primary and otherprocedures, individual procedures, patient sex, patient age, andphysician name, and physician location.
 7. The method of claim 6 whereinsaid data input is provided by an interface.
 8. The method of claim 6wherein said statistical variable results are processed by a pluralityof custom knowledge rules wherein said knowledge rules are comprised ofsimple expressions and wherein said knowledge rules reduce the pluralityof results to a single monetary value, weight value, or priority for thehealthcare visit or alert or both.
 9. The method of claim 8 wherein theresult of said knowledge rule evaluation results in the execution of acustom extension to the process.
 10. The method of claim 1 wherein themonetary value, weight value, or priority of a healthcare visit or alertor both are provided as input to a system with the purpose of aiding auser of said system in performing some follow-up task related to theunderlying issue in said data or with the notification therein of saidalerts and issues.
 11. The method of claim 1, further comprising:determining that the healthcare provider visit has been assigned amonetary value; and associating the monetary value to one or morebusiness rules to create a value processing weight for financialclearance decision making or prioritization of follow up tasks.
 12. Themethod of claim 1, further comprising prioritizing follow-up tasks forthose healthcare provider visits determined to be of highest monetaryvalue.
 13. A hardware storage device operating within a computingarchitecture that includes at least one processor and the hardwarestorage device, wherein the hardware storage device includes codeembodied therein, the code, when executed by the at least one processorcauses the computing architecture to improve how healthcare services aremanaged and provisioned by facilitating generation and selectiveinvocation of an optimized fast query that is generated using anattribute pattern derived from previously aggregated healthcare servicedata and that is used to provide high performance statistical analysiswhen evaluating new healthcare service data, the code comprising: aninstruction for reading historical healthcare visit data associated withone or more healthcare provider visits, the healthcare provider dataincluding at least one previously generated attribute pattern associatedwith one or more past healthcare provider visits, each healthcareprovider visit comprising a combination of professional services andhealthcare items that are to be provided to a patient at the healthcareprovider visit, each professional service and healthcare item having anassociated monetary value; an instruction for performing a statisticalanalysis on the historical healthcare visit data; an instruction forcreation of attribute patterns using statistical variables, theattribute patterns comprising one or more visit-related variablesassociated with the professional services and healthcare items that areto be provided as part of the healthcare provider visit, wherein: thevisit-related variables comprise a selected subset of independent anddependent variables used when calculating a value for the currenthealthcare provider visit, the independent variables are selected basedon their predictive power according to the at least one previouslygenerated attribute pattern, a higher predictive power is manifest wherethe difference between a predicted monetary value and a real monetaryvalue has an optimal Sharpe ratio, a minimum standard deviation, and anaverage approaching zero, the attribute pattern is determined accordingto one or more rules executed within an analytical and rules engine thatprovides a configurable way to augment machine instructions from acompiled language, the analytical and rules engine being configured totake into account environment-specific variables that influence theattribute pattern determination, the machine instructions are executedin the analytical and rules engine along with the one or more rules; anda data representation of the attribute pattern is stored in the hardwarestorage device; an instruction for accepting healthcare data inputsincluding healthcare data inputs for at least one current healthcarevisit; an instruction for generating a fast query that is optimized tospecifically perform one or more attribute pattern queries on thedetermined attribute pattern, the optimized fast query being structuredso that, when executed, it produces a result set that is used tosubsequently identify a match between one or more previously generatedattribute patterns and the determined attribute pattern, and wherein forthe match to be identified based on the fast query's result set, arelationship between the one or more previously generated attributepatterns and the determined attribute pattern is required to satisfy adesignated statistical quality level, wherein the result set isgenerated by executing the optimized fast query on the datarepresentation of the attribute pattern such that the datarepresentation stored in the hardware storage device is accessed duringexecution of the optimized fast query; an instruction to analyze saidhealthcare data inputs for data exceptions and issues; an instruction toraise alerts on said data exceptions; an instruction to use the resultset to identify the match by comparing said healthcare data inputsincluding the at least one previously generated attribute patternagainst said attribute patterns resulting in an assignment of apredicted monetary value, a weight, or a priority to said healthcarevisits including the healthcare visit, or to the alerts, or to both, theweight or priority indicating the order in which follow-up tasksperformed after the healthcare provider visit are to be carried out forthe healthcare provider visit.
 14. The computer-readable storage mediumof claim 13, wherein the historical data is comprised of ANSI ASC X12data, Health Level Seven (HL7 data), Clinical Context Object Workgroup(“CCOW”), or other text-based data from a Healthcare Information,Management, Billing, Scheduling, or Registration system.
 15. Thecomputer-readable storage medium of claim 13, wherein at least one ofthe instructions provides statistical attribute patterns comprisingaverage, mean, maximum, minimum, mode, median, quartiles, standarddeviation, variance, standard error, skew and sample size for charges,contractual discount rates, net payments, copay, and coinsurance. 16.The computer-readable storage medium of claim 13, wherein at least oneof the instructions analyzes attributes for said healthcare visit andits services comprising payer, payer plan, employer group, hospitalname, department, hospital service, services name, diagnostics, primaryand other procedures, individual procedures, patient sex, patient age,and physician name, and physician location.
 17. The computer-readablestorage medium of claim 13, wherein at least one of the instructions iscomprised of rules and simple expressions.
 18. The computer-readablestorage medium of claim 13, wherein at least one of the instructionsuses the said assigned monetary value, weight, or priority of saidvisit, alert, or both to notify a user or to effect some follow-up taskby a user for said alert.
 19. The computer-readable storage medium ofclaim 13, wherein at least one of the instructions comprises a graphicaluser interface in conjunction with said visit or alert or both.
 20. Acomputer system comprising the following: one or more processors; andone or more computer-readable storage media having stored thereoncomputer-executable instructions that are executable by the one or moreprocessors to cause the computer system to improve how healthcareservices are managed and provisioned by facilitating generation andselective invocation of an optimized fast query that is generated usingan attribute pattern derived from previously aggregated healthcareservice data and that is used to provide high performance statisticalanalysis when evaluating new healthcare service data by causing thecomputer system to: instantiate an interface layer that provides aninterface for a user to interact with a remote client, includingaccessing, via the remote client, one or more portions of healthcareprovider data associated with a healthcare provider visit, thehealthcare provider data including at least one previously generatedattribute pattern associated with one or more past healthcare providervisits, the healthcare provider visit comprising a combination ofprofessional services and healthcare items that are to be provided to apatient at the healthcare provider visit, each professional service andhealthcare item having an associated monetary value; determine anattribute pattern that includes one or more visit-related variablesassociated with the professional services and healthcare items that areto be provided as part of the healthcare provider visit, wherein: thevisit-related variables comprise a selected subset of independent anddependent variables used when calculating a value for the currenthealthcare provider visit based on one or more historical healthcareprovider visits, the independent variables are selected based on theirpredictive power according to the at least one previously generatedattribute pattern, a higher predictive power is manifest where adifference between a predicted monetary value and a real monetary valuehas an optimal Sharpe ratio, a minimum standard deviation, and anaverage approaching zero, the attribute pattern is determined accordingto one or more rules executed within an analytical and rules engine thatprovides a configurable way to augment machine instructions from acompiled language, the analytical and rules engine being configured totake into account environment-specific variables that influence theattribute pattern determination, the machine instructions are executedin the analytical and rules engine along with the one or more rules; anda data representation of the attribute pattern is stored in the computersystem's one or more computer-readable storage media; after theattribute pattern is determined, generate a fast query that is optimizedto specifically perform one or more attribute pattern queries on thedetermined attribute pattern, the optimized fast query being structuredso that, when executed, it produces a result set that is used tosubsequently identify a match between one or more previously generatedattribute patterns and the determined attribute pattern, and wherein forthe match to be identified based on the fast query's result set, arelationship between the one or more previously generated attributepatterns and the determined attribute pattern is required to satisfy adesignated statistical quality level; after generating the result set byexecuting the optimized fast query on the data representation of theattribute pattern such that the data representation stored in thecomputer system's one or more computer-readable storage media isaccessed during execution of the optimized fast query, use the resultset to identify the match by comparing the healthcare provider dataassociated with the healthcare provider visit including the at least onepreviously generated attribute pattern to the determined attributepattern to match the at least one previously generated attribute patternwith the newly determined attribute pattern in order to assign amonetary value, a weight, or a priority to the healthcare providervisit; and based on the comparison of the healthcare provider data tothe determined attribute pattern, and prior to the healthcare providervisit taking place, calculate a predicted monetary value, a weight or apriority for the healthcare provider visit, the predicted monetaryvalue, weight or priority indicating the order in which follow-up tasksperformed after the healthcare provider visit are to be carried out forthe healthcare provider visit.